class_docstr: Defines the interface for metrics.
docstr: TF placeholder to be used for the result of this metric.
docstr_tok: ["TF","placeholder","to","be","used","for","the","result","of","this","metric","."]
code: def summary_placeholder(self) -> tf.compat.v1.placeholder:
      """TF placeholder to be used for the result of this metric."""
      if self._summary_placeholder is None:
          result = self.result()
          if not isinstance(result, (np.ndarray, np.generic)):
              result = np.array(result)
          dtype = tf.as_dtype(result.dtype)
          shape = result.shape
          self._summary_placeholder = tf.compat.v1.placeholder(
              dtype, shape=shape, name="{}_ph".format(self.name)
          )
      return self._summary_placeholder
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  from typing import Any, Optional, Sequence, Text, Union
  from absl import logging
  import numpy
  import six
  import tensorflow
  from tf_agents.metrics import tf_metric
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 7 for calling the PyMetric obj's underlying member method code with example usage: obj.summary_placeholder() and returns: self